Knowledge, attitude, and practice of patient-based real-time quality control in Australasia

Patient-based real-time quality control (PBRTQC) involves monitoring an assay using patient samples rather than internal quality control (QC) material (1,2). The principle behind PBRTQC is simple, by monitoring the moving trend (e.g., mean, or median) of the analyte value of patient samples one can infer the ongoing assay performance in real-time (i.e., the performance is monitored as each new result is generated). If the patient population remains stable, then a shift in the moving trend of patient results represents a potential change in the analytical performance of the assay. The advantages of this approach are that the sample(s) are commutable, it is inexpensive, the rules are relatively simple to interpret and there is virtually continuous monitoring of the assay. The disadvantages are that the laboratory needs to understand their patient population and how they may change during the day, week, or year and the initial change of mindset required to adopt the system.

However, whilst there have been several published implementations, it is unclear how this technique is being practiced in the general laboratory (3-5). Recently, there was a workshop organised by the Australian Association of Clinical Biochemists to present different PBRTQC models and to investigate current knowledge and barriers to the implementation of the technique. As part of this workshop, which was attended by 96 participants, a survey of participants was conducted prior to the event to gather information about the knowledge, attitude, and practice of PBRTQC. The questionnaire was structured in the form of a Knowledge, Attitudes, and Practices (KAP) tool.

There were 31 respondents in the survey or a 33% response rate. The respondents provided informed consent prior to the start of the survey. There were 15 closed-ended Likert-scale items and three open-ended questions in the survey. The results of the survey are aggregated and summarized. No individually identifiable information was collected. The responses to the survey questions are given in Table 1 (Knowledge), Table 2 (Attitudes), and Table 3 (Practices) below.

When asked about what the respondents feel is the main gap in their knowledge regarding PBRTQC, 55% answered a lack of experience with the parameters of PBRTQC whereas 32% reported a lack of basic knowledge of PBRTQC. Additionally, in the 12 months preceding the survey, 45% of the respondents had felt PBRTQC would have been useful to their laboratory’s quality strategy. These included instrument issues that would have been detected earlier with PBRTQC, assays that drift between scheduled QC samples, changes in reagent lots not detected by conventional QC, and new lot number of QC material. When asked about the barriers and challenges in adopting PBRTQC in the respondent’s laboratory, 32% reported informatics or middleware availability as the main hindrance while 23% mentioned a lack of experience or knowledge in this area of practice.

PBRTQC is very topical, but there is no information available about the knowledge, attitude, practice, and barriers to implementation. This survey provides the first structured data collection process that can help fill that information gap.

When we examine the structured component of the survey, we find that there is a reasonable knowledge of the benefits and theory of PBRTQC. There is a positive attitude to the advantages of a PBRTQC implementation, but little experience with setting parameters. There was a 40% expectation of implementing PBRTQC in the respondents’ laboratories. There is also enough interest that the literature is being read by participants, so there is an awareness of the concepts. This was also reflected in the large number of attendees at the workshop, about one-third of those at the conference to which the workshop was attached.

In the open questions, the lack of experience with setting up PBRTQC was again emphasized, though 45% could see where the implementation would have been useful in the last year. These comments related to failures of conventional QC to detect significant error due to poor QC strategy (6,7). The major limitation to implementation is the lack of software/middleware available to implement PBRTQC. The requirements are well defined (8,9); however, there is an opportunity for suppliers of instrumentation to offer PBRTQC and other support software that can improve patient outcomes and laboratory efficiency (10).

The limitations of this survey were a bias in the motivation of attendees at the workshop in that they were interested in PBRTQC and the relatively low response rate.

There is no doubt that PBRTQC represents a disruptive technique that will improve QC systems and patient outcomes. The barriers to implementation are education, which the professional associations are providing, and onboard software to provide the routines needed. The profession needs the active support of the manufacturers of analytical systems and middleware providers to fill this implementation gap.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was a standard submission to the journal. The article has undergone external peer review.

Peer Review File: Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-23-14/prf

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-23-14/coif). TB serves as an unpaid editorial board member of Journal of Laboratory and Precision Medicine from February 2023 to January 2025. The other author has no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Badrick T, Bietenbeck A, Cervinski MA, et al. Patient-Based Real-Time Quality Control: Review and Recommendations. Clin Chem 2019;65:962-71. [Crossref] [PubMed]
2. van Rossum HH, Bietenbeck A, Cervinski MA, et al. Benefits, limitations and controversies on patient-based real-time quality control (PBRTQC) and the evidence behind the practice. Clin Chem Lab Med 2021;59:1213-20. [Crossref] [PubMed]
3. Lukić V, Ignjatović S. Optimizing moving average control procedures for small-volume laboratories: can it be done? Biochem Med (Zagreb) 2019;29:030710. [Crossref] [PubMed]
4. van Andel E, Henricks LM, Giliams APM, et al. Moving average quality control of routine chemistry and hematology parameters - a toolbox for implementation. Clin Chem Lab Med 2022;60:1719-28. [Crossref] [PubMed]
5. Katayev A, Fleming JK. Past, present, and future of laboratory quality control: patient-based real-time quality control or when getting more quality at less cost is not wishful thinking. J Lab Precis Med 2020;5:28. [Crossref]
6. Howanitz PJ, Tetrault GA, Steindel SJ. Clinical laboratory quality control: a costly process now out of control. Clin Chim Acta 1997;260:163-74. [Crossref] [PubMed]
7. WestgardQC. Survey of UK IQC practices. 2011. Available online: https:///www.westgard.com/downloads/other-downloads/60-2011-survey-of-uk-iqc-practices.html
8. Loh TP, Cervinski MA, Katayev A, et al. Recommendations for laboratory informatics specifications needed for the application of patient-based real time quality control. Clin Chim Acta 2019;495:625-9. [Crossref] [PubMed]
9. Bietenbeck A, Cervinski MA, Katayev A, et al. Understanding Patient-Based Real-Time Quality Control Using Simulation Modeling. Clin Chem 2020;66:1072-83. [Crossref] [PubMed]
10. Brown AS, Badrick T. The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance. Clin Chem Lab Med 2022;61:37-43. [Crossref] [PubMed]